Tomographic imaging methods are characterized by enabling the internal structures of an object under examination to be examined without having to carry out operational interventions on said structures. One possible type of tomographic imaging consists of capturing a number of projections from different angles. A two-dimensional slice image or a three-dimensional volume image of the object under examination can be computed from these projections.
Computed tomography is an example of this type of tomographic imaging method. Methods for scanning an object under examination with a CT system are generally known. Typical methods employed in such cases are orbital scans, sequential orbital scans with advance or spiral scans. Other types of scans which are not based on orbital movements are possible, such as scans with linear segments for example.
Absorption data of the object under examination is recorded from different imaging angles with the aid of at least one X-ray source and at least one detector lying opposite said source and this absorption data or projections collected in this way are computed by way of appropriate reconstruction methods into image slices through the object under examination.
For reconstruction of computed-tomographic images from X-ray CT datasets of a computed-tomography (CT) device, i.e. from the captured projections, what is known as a Filtered Back Projection (FBP) is used nowadays as the standard method. After the data has been recorded, a so-called “rebinning” step is executed in which the data generated with the beam spreading out in the form of a fan is rearranged such that it is available in a form such as would occur had the detector been hit by X-rays arriving at the detector in parallel. The data is then transformed into the frequency range. A filtering is undertaken in the frequency range and subsequently the filtered data is back transformed. With the aid of the data sorted out and filtered in this way a back projection is then carried out onto the individual voxels within the volume of interest. However, because of the approximative way in which they operate, problems arise with the classical FBP methods with so-called cone-beam artifacts and spiral artifacts. Furthermore image sharpness is coupled to image noise in classical FBP methods. The higher is the sharpness obtained, the higher is also the image noise, and vice versa.
Iterative reconstruction methods have thus been recently developed, with which at least some of these limitations can be overcome. In such an iterative reconstruction method initial image data is first reconstructed from the projection measurement data. A folding back projection method can typically be used for this purpose. Synthetic projection data is then generated from this initial image data using a “projector”, which is designed to map the measuring system as well as possible.
The difference from the measurement signals is then back projected with the operator adjoined to the projector and a residuum image is reconstructed with which the initial image is updated. The updated image data can be used in its turn to generate new synthetic projection data in the next iteration step with the aid of the projection operator, to once again form the difference from the measurement signals from said data and to compute a new residuum image with which the image data of the current iteration step can again be improved etc. Such a method enables image data to be reconstructed which exhibits relatively good image sharpness yet still produces low-noise images. Examples of iterative reconstruction methods are algebraic reconstruction technique (ART), simultaneous algebraic reconstruction technique (SART), iterated filtered back projection (IFBP), or also statistical iterative image reconstruction techniques.